Correction: Anti-Bacterial Effects of Poly-N-Acetyl-Glucosamine Nanofibers in Cutaneous Wound Healing: Requirement for Akt1
Bibliographic record
Abstract
Background: Treatment of cutaneous wounds with poly-N-acetyl-glucosamine nanofibers (sNAG) results in increased kinetics of wound closure in diabetic animal models, which is due in part to increased expression of several cytokines, growth factors, and innate immune activation.Defensins are also important for wound healing and anti-microbial activities.Therefore, we tested whether sNAG nanofibers induce defensin expression resulting in bacterial clearance. Methodology:The role of sNAG in defensin expression was examined using immunofluoresence microscopy, pharmacological inhibition, and shRNA knockdown in vitro.The ability of sNAG treatment to induce defensin expression and bacterial clearance in WT and AKT12/2 mice was carried out using immunofluoresent microscopy and tissue gram staining.Neutralization, using an antibody directed against b-defensin 3, was utilized to determine if the antimicrobial properties of sNAG are dependent on the induction of defensin expression.Conclusions/Findings: sNAG treatment causes increased expression of both aand b-type defensins in endothelial cells and b-type defensins in keratinocytes.Pharmacological inhibition and shRNA knockdown implicates Akt1 in sNAG-dependent defensin expression in vitro, an activity also shown in an in vivo wound healing model.Importantly, sNAG treatment results in increased kinetics of wound closure in wild type animals.sNAG treatment decreases bacterial infection of cutaneous wounds infected with Staphylococcus aureus in wild type control animals but not in similarly treated Akt1 null animals.Furthermore, sNAG treatment of S. aureus infected wounds show an increased expression of b-defensin 3 which is required for sNAG-dependent bacterial clearance.Our findings suggest that Akt1 is involved in the regulation of defensin expression and the innate immune response important for bacterial clearance.Moreover, these findings support the use of sNAG nanofibers as a novel method for enhancing wound closure while simultaneously decreasing wound infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".